A Multi-Sensor Mini-Bioreactor to Preselect Silage Inoculants by Tracking Metabolic Activity in situ During Fermentation

• Main Authors: Guilin Shan, Victoria Rosner, Andreas Milimonka, Wolfgang Buescher, André Lipski, Christian Maack, Wilfried Berchtold, Ye Wang, David A. Grantz, Yurui Sun
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-08-01
• Series: Frontiers in Microbiology
• Subjects: lactic acid bacteria (LAB); multi-sensor mini-bioreactor (MSMB); fermentation; silage additive; metabolic sensitivity; pH
• Online Access: https://www.frontiersin.org/articles/10.3389/fmicb.2021.673795/full

The microbiome in silage may vary substantially from the onset to the completion of fermentation. Improved additives and inoculants are being developed to accelerate the ensiling process, to enhance fermentation quality, and to delay spoilage during feed-out. However, current methods for preselecting and characterizing these amendments are time-consuming and costly. Here, we have developed a multi-sensor mini-bioreactor (MSMB) to track microbial fermentation in situ and additionally presented a mathematical model for the optimal assessment among candidate inoculants based on the Bolza equation, a fundamental formula in optimal control theory. Three sensors [pH, CO2, and ethanol (EtOH)] provided data for assessment, with four additional sensors (O2, gas pressure, temperature, and atmospheric pressure) to monitor/control the fermentation environment. This advanced MSMB is demonstrated with an experimental method for evaluating three typical species of lactic acid bacteria (LAB), Lentilactobacillus buchneri (LB) alone, and LB mixed with Lactiplantibacillus plantarum (LBLP) or with Enterococcus faecium (LBEF), all cultured in De Man, Rogosa, and Sharpe (MRS) broth. The fermentation process was monitored in situ over 48 h with these candidate microbial strains using the MSMB. The experimental results combine acidification characteristics with production of CO2 and EtOH, optimal assessment of the microbes, analysis of the metabolic sensitivity to pH, and partitioning of the contribution of each species to fermentation. These new data demonstrate that the MSMB associated with the novel rapid data-processing method may expedite development of microbial amendments for silage additives.